# Refactored run_webcam.py #608
# https://github.com/ildoonet/tf-pose-estimation/pull/608
def str2bool(v):
    if type(v) == bool:
        return v
    return v.lower() in ("yes", "true", "t", "1")

if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser(description='tf-pose-estimation realtime webcam')
    parser.add_argument('--camera', type=int, default=0, help='A filename or a device for the camera input stream. default=0')

    parser.add_argument('--resize', type=str, default='0x0',
                        help='if provided, resize images before they are processed. default=0x0, Recommends : 432x368 or 656x368 or 1312x736 ')
    parser.add_argument('--resize-out-ratio', type=float, default=4.0,
                        help='if provided, resize heatmaps before they are post-processed. default=1.0')

    parser.add_argument('--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin / mobilenet_v2_large / mobilenet_v2_small. default=mobilenet_thin')
    parser.add_argument('--show-process', type=bool, default=False,
                        help='for debug purpose, if enabled, speed for inference is dropped. default=False')
    
    parser.add_argument('--tensorrt', type=bool, default=False,
                        help='for tensorrt process. default=False')
    args = parser.parse_args()

    import logging
    import time

    import cv2
    import numpy as np

    from tf_pose.estimator import TfPoseEstimator
    from tf_pose.networks import get_graph_path, model_wh

    logger = logging.getLogger('TfPoseEstimator-WebCam')
    logger.setLevel(logging.DEBUG)
    ch = logging.StreamHandler()
    ch.setLevel(logging.DEBUG)
    formatter = logging.Formatter('[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s')
    ch.setFormatter(formatter)
    logger.addHandler(ch)
  
    fps_time = 0
    f = 0

    logger.debug('initialization %s : %s' % (args.model, get_graph_path(args.model)))
    w, h = model_wh(args.resize)
    if w > 0 and h > 0:
        e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h), trt_bool=str2bool(args.tensorrt))
    else:
        e = TfPoseEstimator(get_graph_path(args.model), target_size=(432, 368), trt_bool=str2bool(args.tensorrt))
    logger.debug('cam read+')
    cam = cv2.VideoCapture(args.camera)
    ret_val, image = cam.read()
    logger.info('cam image=%dx%d' % (image.shape[1], image.shape[0]))

    import matplotlib.pyplot as plt

    while True:
        ret_val, image = cam.read()
        if not ret_val:
            logger.error('Something went wrong with the camera.')
            break

        logger.debug('image process+')
        humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=args.resize_out_ratio)

        logger.debug('postprocess+')
        image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)

        logger.debug('show+')
        cv2.putText(image,
                    "FPS: %f" % (1.0 / (time.time() - fps_time)),
                    (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                    (0, 255, 0), 2)
        # cv2.imshow('tf-pose-estimation result', image)
        png_filename = 'example_webcam_'+ str(f).zfill(5) +'.png'
        plt.imsave(png_filename, cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
        f = f + 1
        if f > 1000:
            break
        # print('*', end='')
        fps_time = time.time()
        if cv2.waitKey(1) == 27:
            break
        logger.debug('finished+')

    cv2.destroyAllWindows()
